Needle in a Haystack: Tracking UAVs from Massive Noise in Real-World 5G-A Base Station Data
Chengzhen Meng, Chenming He, Yidong Jiang, Xiaoran Fan, Dequan Wang, Lingyu Wang, Jianmin Ji, Yanyong Zhang

TL;DR
This paper presents BSense, a novel system that tracks UAVs using commercial 5G-A base station point clouds, effectively filtering noise and false detections to achieve high accuracy in urban environments.
Contribution
We introduce a layered filtering framework leveraging signal fingerprints, spatial and velocity consistency, and Transformer-based motion pattern analysis for UAV tracking.
Findings
Reduces false detections from 168.05 to 0.04 per frame
Achieves 95.56% F1 score in urban UAV tracking
Localizes UAVs with an average error of 4.9 meters at 1,000 meters range
Abstract
The potential usage of UAVs in daily life has made monitoring them essential. However, existing systems for monitoring UAVs typically rely on cameras, LiDARs, or radars, whose limited sensing range or high deployment cost hinder large-scale adoption. In response, we develop BSense, the first system that tracks UAVs by leveraging point clouds from commercial 5G-A base stations. The key challenge lies in the dominant number of noise points that closely resemble true UAV points, resulting in a noise-to-UAV ratio over 100:1. Therefore, identifying UAVs from the raw point clouds is like finding a needle in a haystack. To overcome this, we propose a layered framework that filters noise at the point, object, and trajectory levels. At the raw point level, we observe that noise points from different spatial regions exhibit distinguishable and consistent signal fingerprints, which we can model to…
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